Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph neural networks have shown significant success in the field of graph representation
learning. Graph convolutions perform neighborhood aggregation and represent one of the …

Graph convolutional network (GCN) is an effective neural network model for graph
representation learning. However, standard GCN suffers from three main limitations:(1) most …

Convolutional neural networks (CNNs) leverage the great power in representation learning
on regular grid data such as image and video. Recently, increasing attention has been paid …

Graphs are useful data structures in complex real-life applications such as modeling
physical systems, learning molecular fingerprints, controlling traffic networks, and …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …